Research

20 pages, 5384 KiB

Open AccessArticle

Evaluation of the Bioenergy Potential of Blends (Green Coconut Shells and Fish Scales) as a Feedstock in Thermochemical Processes for Clean Energy Production

by Ayrton Pablo Raiol Monroe, Arthur Vinicius Sousa Silva, Mariana Silva Melo, Julie Brenda Santos da Silva, Ramón Raudel Peña Garcia, Maria Alexsandra de Sousa Rios, Waldir Antônio Bizzo and Glauber Cruz

Processes 2024, 12(4), 710; https://doi.org/10.3390/pr12040710 - 30 Mar 2024

Viewed by 505

Abstract

Brazil is among the world’s largest producers of green coconut, which contributes to inappropriate disposal and socioenvironmental impacts. Concomitantly, some of its coastal cities produce a great diversity of fish and large amounts of solid waste. This paper reports on the use of [...] Read more.

Brazil is among the world’s largest producers of green coconut, which contributes to inappropriate disposal and socioenvironmental impacts. Concomitantly, some of its coastal cities produce a great diversity of fish and large amounts of solid waste. This paper reports on the use of samples of fish scales (100FS) and green coconut shells (100GCS) and their mixtures in 75%FS:25%GCS (B25), 50%FS:50%GCS (B50), and 25%FS:75%GCS (B75) proportions and quantification of their Higher Heating Values (HHV) and Lower Heating Values (LHV), and Ultimate (UA) and Proximate Analyses (PA). Their thermal behavior was investigated by thermogravimetry (TG/DTG) and differential scanning calorimetry (DSC), whereas scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), and Fourier transformed infrared (FTIR) were employed for analyses of their physicochemical and morphostructural properties. When compared to in natura samples, SEM images of the blends detected a structural disorder and a highly fibrous structure with an elongated chain and surface roughness. HHV were superior in samples with 100GCS (16.64 MJ kg⁻¹), B75 (15.80 MJ kg⁻¹), and B50 (14.98 MJ kg⁻¹), and lower in B25 (14.16 MJ kg⁻¹) and 100FS (13.03 MJ kg⁻¹), with acceptable values for different biomasses. TG/DTG and DSC curves showed similarities among the samples, with the detection of their main thermoconversion stages. According to the data, the samples can be applied as renewable energy sources to mitigate socioecological illnesses and social vulnerabilities resulting from the archaic and inadequate management of solid waste. Full article

(This article belongs to the Special Issue 10th Anniversary of Processes: Feature Papers in Chemical Processes and Systems)

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15 pages, 3716 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Study of Mid-Pressure Ar Radiofrequency Plasma Used in Plasma-Enhanced Atomic Layer Deposition of α-Al₂O₃

by Carl-Thomas Piller, Jüri Raud, Lauri Aarik, Indrek Jõgi, Rasmus Talviste and Jaan Aarik

Processes 2024, 12(3), 612; https://doi.org/10.3390/pr12030612 - 20 Mar 2024

Viewed by 495

Abstract

This study investigated the characteristics of radiofrequency, middle-pressure argon plasma used in the atomic layer deposition (ALD) of Al₂O₃ films. Based on the electrical characteristics—the current, voltage, and phase shift between them—and the stability of the plasma plume, the optimum [...] Read more.

This study investigated the characteristics of radiofrequency, middle-pressure argon plasma used in the atomic layer deposition (ALD) of Al₂O₃ films. Based on the electrical characteristics—the current, voltage, and phase shift between them—and the stability of the plasma plume, the optimum plasma power, allowing reliable switching on of the plasma for any step of an ALD cycle, was determined. Spectral measurements were performed to determine the gas temperature and reactive species that could be important in the ALD process. The density of metastable argon atoms was estimated using tunable laser absorption spectroscopy. It was concluded that plasma heating of substrates did not affect film growth. The crystallization-enhancing effect of plasma observed in these experiments was due to the action of OH radicals produced in the plasma. Full article

(This article belongs to the Special Issue 10th Anniversary of Processes: Feature Papers in Chemical Processes and Systems)

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18 pages, 3035 KiB

Open AccessArticle

Data-Based Modeling, Multi-Objective Optimization and Multi-Criteria Decision Making of a Catalytic Ozonation Process for Degradation of a Colored Effluent

by Seyed Reza Nabavi, Saheleh Ghahri and Gade Pandu Rangaiah

Processes 2024, 12(3), 515; https://doi.org/10.3390/pr12030515 - 03 Mar 2024

Viewed by 565

Abstract

In the catalytic ozonation process (COP), the reactions are complex, and it is very difficult to determine the effect of different operating parameters on the degradation rate of pollutants. Data-based modeling tools, such as the multilayer perceptron (MLP) neural network, can be useful [...] Read more.

In the catalytic ozonation process (COP), the reactions are complex, and it is very difficult to determine the effect of different operating parameters on the degradation rate of pollutants. Data-based modeling tools, such as the multilayer perceptron (MLP) neural network, can be useful in establishing the complex relationship of degradation efficiency with the operating variables. In this work, the COP of acid red 88 (AR88) with Fe₃O₄ nano catalyst was investigated in a semi-batch reactor and a MLP model was developed to predict the degradation efficiency (%DE) of AR88 in the range of 25 to 96%. The MLP model was trained using 78 experimental data having five input variables, namely, AR88 initial concentration, catalyst concentration, pH, inlet air flow rate and batch time (in the ranges of 150–400 mg L⁻¹, 0.04–0.4 g L⁻¹, 4.5–8.5, 0.5–1.90 mg min⁻¹ and 5–30 min, respectively). Its optimal topology was obtained by changing the number of neurons in the hidden layer, the momentum and the learning rates to 7, 0.075 and 0.025, respectively. A high correlation coefficient (R² > 0.98) was found between the experimental and predicted values by the MLP model. Simultaneous maximization of %DE and minimization of Fe₃O₄ concentration was carried out by multi-objective particle swarm optimization (MOPSO) and the Pareto-optimal solutions were successfully obtained. The trade-off was analyzed through multi-criteria decision making, and one Pareto-optimal solution was selected. The developed model and optimal points are useful for treatment of AR88 wastewater. Full article

(This article belongs to the Special Issue 10th Anniversary of Processes: Feature Papers in Chemical Processes and Systems)

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13 pages, 438 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Coke Substitution with Anthracite in Sintering Production

by Pavlina Pustejovska, Michal Sikora, Simona Jursova, Kristyna Pustejovska and Edyta Kardas

Processes 2024, 12(3), 440; https://doi.org/10.3390/pr12030440 - 22 Feb 2024

Viewed by 765

Abstract

This article summarises the possibility of replacing the coke breeze sintering fuel with an economically and ecologically more suitable fuel, anthracite. The main focus is on the possibility of replacing coke breeze with anthracite, during which, the replacement process is accelerated and the [...] Read more.

This article summarises the possibility of replacing the coke breeze sintering fuel with an economically and ecologically more suitable fuel, anthracite. The main focus is on the possibility of replacing coke breeze with anthracite, during which, the replacement process is accelerated and the other properties are also affected. The analyses performed showed that the replacement of coke breeze with different amounts of anthracite does not have a negative effect if the initial permeability of the sintering bed is the same. Full article

(This article belongs to the Special Issue 10th Anniversary of Processes: Feature Papers in Chemical Processes and Systems)

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15 pages, 1190 KiB

Open AccessFeature PaperArticle

Mechanical Recycling of Post-Industrial PC/ABS Blends from the Automotive Sector by Mixture Design

by Silvia Zanatta, Carlo Boaretti, Eleonora Dal Lago, Luca Scopel, Diego Penzo and Michele Modesti

Processes 2024, 12(2), 349; https://doi.org/10.3390/pr12020349 - 07 Feb 2024

Viewed by 1065

Abstract

The widespread use of plastic materials poses significant environmental challenges, which have promoted a growing call for a circular approach to such materials, emphasizing waste reintegration into production. From this perspective, the present study focuses on analyzing a post-industrial recycled stream of chrome-plated [...] Read more.

The widespread use of plastic materials poses significant environmental challenges, which have promoted a growing call for a circular approach to such materials, emphasizing waste reintegration into production. From this perspective, the present study focuses on analyzing a post-industrial recycled stream of chrome-plated automotive scraps composed of acrylonitrile–butadiene–styrene (ABS), polycarbonate (PC), and a commercial PC/ABS blend. The batch-to-batch varying proportions of these constituents make control of the recycled materials’ quality challenging. To address this problem, we formulated an I-optimal mixture design model using the infrared absorbance ratio of selected peaks and Izod impact resistance results for injection-molded samples as inputs to determine the composition of each batch quickly. The two model equations (adjusted R² > 0.97) obtained, based on cubic expression and regressed from data obtained from 27 mixtures with known compositions, were combined into a single system for the determination of unknown batch compositions. The validation test showed that the models can satisfactorily predict both the infrared and Izod results of blends with known compositions, but in the latter case, the results are less reliable, probably due to specific blend morphological interactions according to specific compositions. The results can improve mechanical recycling methodologies for reintroducing plastic wastes to the market. Full article

(This article belongs to the Special Issue 10th Anniversary of Processes: Feature Papers in Chemical Processes and Systems)

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21 pages, 3891 KiB

Open AccessFeature PaperArticle

Advancing Process Control in Fluidized Bed Biomass Gasification Using Model-Based Deep Reinforcement Learning

by Ibtihaj Khurram Faridi, Evangelos Tsotsas and Abdolreza Kharaghani

Processes 2024, 12(2), 254; https://doi.org/10.3390/pr12020254 - 24 Jan 2024

Viewed by 674

Abstract

This study presents a model-based deep reinforcement learning (MB-DRL) controller for the fluidized bed biomass gasification (FBG) process. The MB-DRL controller integrates a deep neural network (DNN) model and a reinforcement learning-based optimizer. The DNN model is trained with operational data from a [...] Read more.

This study presents a model-based deep reinforcement learning (MB-DRL) controller for the fluidized bed biomass gasification (FBG) process. The MB-DRL controller integrates a deep neural network (DNN) model and a reinforcement learning-based optimizer. The DNN model is trained with operational data from a pilot-scale FBG plant to approximate FBG process dynamics. The reinforcement learning-based optimizer employs a specially designed reward function, determining optimal control policies for FBG. Moreover, the controller includes an online learning component, ensuring periodic updates to the DNN model training. The performance of the controller is evaluated by testing its control accuracy for regulating synthetic gas composition, flow rate, and CO concentration in the FBG. The evaluation also includes a comparison with a model predictive controller. The results demonstrate the superior control performance of MB-DRL, surpassing MPC by over 15% in regulating synthetic gas composition and flow rate, with similar effectiveness observed in synthetic gas temperature control. Additionally, this study also includes systematic investigations into factors like DNN layer count and learning update intervals to provide insights for the practical implementation of the controller. The results, presenting a 50% reduction in control error with the addition of a single layer to the DNN model, highlight the significance of optimizing MB-DRL for effective implementation. Full article

(This article belongs to the Special Issue 10th Anniversary of Processes: Feature Papers in Chemical Processes and Systems)

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12 pages, 5760 KiB

Open AccessArticle

Preparation of High Specific Surface Area Activated Carbon from Petroleum Coke by KOH Activation in a Rotary Kiln

by Kechao Wang and Shaoping Xu

Processes 2024, 12(2), 241; https://doi.org/10.3390/pr12020241 - 23 Jan 2024

Viewed by 719

Abstract

In the preparation of high specific surface area activated carbon (AC) by KOH activation, the swelling of the reactant mixture and the particles’ agglomeration deteriorates the process and the property of product. In this study, a novel method using a rotary kiln loaded [...] Read more.

In the preparation of high specific surface area activated carbon (AC) by KOH activation, the swelling of the reactant mixture and the particles’ agglomeration deteriorates the process and the property of product. In this study, a novel method using a rotary kiln loaded with steel balls has been developed for the preparation of AC from petroleum coke (PC) by KOH activation. It has been found that the molten KOH caused the swelling of the reaction mixture at a lower activation temperature, while the molten K₂O led to the particles’ agglomeration at a higher temperature. The steel balls could relieve the swelling and agglomeration and enhance the pore structure development of the AC by boosting the heat and mass transfer in the reactor. At an activation temperature of 800 °C and a KOH/PC mass ratio of 3:1, the specific surface area of the AC obtained without the addition of steel balls in the kiln is 1492 m²/g, while that with the steel balls is 1996 m²/g. The introduction of CO₂ during the activation could further decrease the particles’ agglomeration by converting the K₂O into thermoset K₂CO₃ and develop more mesopores of AC. Specifically, the average pore size of the AC increased from 2.20 to 2.72 nm. Full article

(This article belongs to the Special Issue 10th Anniversary of Processes: Feature Papers in Chemical Processes and Systems)

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22 pages, 615 KiB

Open AccessFeature PaperArticle

The Nutritional Value of Plant Drink against Bovine Milk—Analysis of the Total Concentrations and the Bio-Accessible Fraction of Elements in Cow Milk and Plant-Based Beverages

by Maja Welna, Anna Szymczycha-Madeja, Anna Lesniewicz and Pawel Pohl

Processes 2024, 12(1), 231; https://doi.org/10.3390/pr12010231 - 21 Jan 2024

Viewed by 799

Abstract

Four types of non-dairy (plant) drinks—almond, oat, rice, and soy—as well as cow milk with varying fat contents (1.5%, 2.0%, and 3.2%), were examined and compared in terms of the total concentrations of Al, As, B, Ba, Ca, Cd, Cr, Cu, Fe, K, [...] Read more.

Four types of non-dairy (plant) drinks—almond, oat, rice, and soy—as well as cow milk with varying fat contents (1.5%, 2.0%, and 3.2%), were examined and compared in terms of the total concentrations of Al, As, B, Ba, Ca, Cd, Cr, Cu, Fe, K, Mg, Na, Mn, Ni, P, Pb, Sb, Se, Sr, and Zn using inductively coupled optical emission spectrometry (ICP OES). Additionally, in vitro gastrointestinal digestion was used to determine the bio-accessible fraction of selected elements, evaluating the nutritional value and risk assessment involved with the consumption of these beverages. A significant difference in the mineral profile was observed depending on the type of plant drink, with the highest content of elements noted in the soy drink and the lowest in the rice drink. Except for Ca and P, the soy drink appears to be a much better source of essential nutrients, including Cu, Fe, and Mn, than cow’s milk. A similar Ca content in plant beverages can be obtained only by adding calcium salt at the stage of its production. Interestingly, by using the multivariate data analysis, the average content of the selected elements (Cu, K, Na, P, and Zn) can be used both to differentiate dairy and non-dairy milk samples according to their type and to distinguish plant drinks from milk of animal origin. The bio-accessibility of essential elements (Ca, Cu, Fe, Mg, Mn, P, Zn) in cow milk was within 8.37–98.2% and increased with an increase in its fat content. Accordingly, by drinking 1 L of this milk daily, it is possible to contribute to the recommended dietary intakes of Ca, P, Cu, Mg, and Zn between 5.6–68%. Although the bio-accessibility of elements in the rice drink was the highest (9.0–90.8%), the soy drink seems to be the best source of nutrients in bioavailable forms; its consumption (1 L/day) covers the requirements of Cu, Mn, Mg, Ca, P, and Zn in 7.0–67%. Unfortunately, both groups of beverages are not important sources of Fe (plant drink) and Mn or Fe (cow milk) in the human diet. On the other hand, potentially toxic elements (Al, B, Ba) were found in them in a relatively inert form. Full article

(This article belongs to the Special Issue 10th Anniversary of Processes: Feature Papers in Chemical Processes and Systems)

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20 pages, 4080 KiB

Open AccessArticle

Effects of Injection Parameters and EHN Mixing on the Combustion Characteristics of Fueling Pure Methanol in a Compression Ignition Engine

by Haifeng Liu, Mengjia Li, Hongyuan Wei, Can Wang, Tengda Song, Zhixiong Huang, Zhao Zhang, Yanqing Cui and Chao Jin

Processes 2024, 12(1), 48; https://doi.org/10.3390/pr12010048 - 24 Dec 2023

Cited by 1 | Viewed by 649

Abstract

As one of the most ideal alternative fuels for internal combustion engines, methanol can achieve near-zero carbon emissions. The main problem of methanol application in compression combustion engines is the phase lag caused by its poor combustion characteristics, but under low load conditions, [...] Read more.

As one of the most ideal alternative fuels for internal combustion engines, methanol can achieve near-zero carbon emissions. The main problem of methanol application in compression combustion engines is the phase lag caused by its poor combustion characteristics, but under low load conditions, the fuel activity can be improved by adding the cetane number improver EHN (Isooctyl nitrate), and the dependence on intake heating can be reduced to a certain extent. Based on a three-dimensional CFD simulation, the effects of methanol injection parameters and the addition of EHN on the combustion characteristics of a four-stroke exhaust turbocharged diesel engine were studied in this paper. With or without EHN, the increase in injection pressure and the advance in injection timing lead to an increase in the peak temperature, pressure, and heat release rate, as well as a shortening of the combustion duration. Adding EHN witnesses reduced requirements for methanol ignition, including a decreased peak temperature, pressure, and heat release rate, a significantly shortened ignition delay period, and an extended combustion duration, which thus results in a reduced indicated thermal efficiency. This study innovatively develops a 3D model of a compression combustion engine applicable to in-cylinder direct injection pure methanol fuel and EHN under small load conditions, which provides a reference for future research and development of small-load pure methanol compression combustion engines and has certain guiding significance. Full article

(This article belongs to the Special Issue 10th Anniversary of Processes: Feature Papers in Chemical Processes and Systems)

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13 pages, 5912 KiB

Open AccessArticle

An Effective Mercury Ion Adsorbent Based on a Mixed-Matrix Polyvinylidene Fluoride Membrane with Excellent Hydrophilicity and High Mechanical Strength

by Ling Cao, Xia Wu, Fajun He, Xianfeng Meng, Wei He, Jing Li, Guidan Zhu, Hehua Zeng and Chuanyi Wang

Processes 2024, 12(1), 30; https://doi.org/10.3390/pr12010030 - 22 Dec 2023

Viewed by 682

Abstract

Improving the hydrophilicity and mechanical strength of membranes in water treatment applications remains challenging. In this study, modified vermiculite (VT-M) and a hydrophilic polyvinylpyrrolidone (PVP) were introduced into a polyethyleneimine-functionalized polyvinylidene fluoride composite membrane (PVDF/PEI) to prepare a comprehensively modified mixed-matrix PVDF composite [...] Read more.

Improving the hydrophilicity and mechanical strength of membranes in water treatment applications remains challenging. In this study, modified vermiculite (VT-M) and a hydrophilic polyvinylpyrrolidone (PVP) were introduced into a polyethyleneimine-functionalized polyvinylidene fluoride composite membrane (PVDF/PEI) to prepare a comprehensively modified mixed-matrix PVDF composite membrane adsorbent that exhibited high mechanical strength and excellent hydrophilicity. The modified composite membrane featured good tensile properties, with a maximum tensile strength of 2.093 MPa, which was 2.5 times that of the PVDF/PEI membrane. After 7 s, the water contact angle of the composite membrane decreased to 0°, leading to significantly improved hydrophilicity. The modified composite membrane exhibited excellent adsorption selectivity for mercury ions, with a fitted maximum adsorption capacity of 807 mg/g. In a mixed-metal ion solution, the selectivity of the membrane for Hg(II) ions was 1.2 × 10⁵ times that for Cd(II) ions. The adsorption mechanism of Hg(II) ions involved chelation, electrostatic attraction, and crystal growth processes. The present work suggests the great potential of mixed-matrix PVDF composite materials in the remediation of mercury-polluted water environments. Full article

(This article belongs to the Special Issue 10th Anniversary of Processes: Feature Papers in Chemical Processes and Systems)

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